{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import signal\n",
    "import cmath\n",
    "nsamples = 8_192\n",
    "fs = 44_100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_signal(samples = 2048, fs = 44_100):\n",
    "    f0, f1, f2, f3, f4 = 1_000, 4_000, 6_000, 8_000, 17_357\n",
    "    ts = 1 / fs\n",
    "    time = np.linspace(0, samples*ts, samples)\n",
    "\n",
    "    tone_0 = 1 * np.sin(2*np.pi*f0*time)\n",
    "    tone_1 = 0.8 * np.sin(2*np.pi*f1*time)\n",
    "    tone_2 = 2 * np.sin(2*np.pi*f2*time)\n",
    "    tone_3 = 0.1 * np.sin(2*np.pi*f3*time)\n",
    "    tone_4 = 0.3* np.sin(2*np.pi*f4*time)\n",
    "    signal = tone_0 + tone_1 + tone_2*1j + tone_3*1j + tone_4*1j\n",
    "    return signal\n",
    "\n",
    "def compute_fft(data, fs = 44_100):\n",
    "    samples = len(data)\n",
    "    ts = 1 / fs\n",
    "    spectrum = np.fft.fft(data)\n",
    "    freqs = np.fft.fftshift(np.fft.fftfreq(samples, d=ts))\n",
    "    return spectrum, freqs\n",
    "\n",
    "def init(n):\n",
    "    omg = [cmath.exp(-2j * cmath.pi * i / n) for i in range(n)]\n",
    "    return omg\n",
    "\n",
    "def fft(x):\n",
    "    a = np.copy(x)\n",
    "    n = len(a)\n",
    "    omg = init(n)\n",
    "\n",
    "    lim = 0\n",
    "    while (1 << lim) < n:\n",
    "        lim += 1\n",
    "\n",
    "    for i in range(n):\n",
    "        t = 0\n",
    "        for j in range(lim):\n",
    "            if ((i >> j) & 1):\n",
    "                t |= (1 << (lim - j - 1))\n",
    "        if i < t:\n",
    "            a[i], a[t] = a[t], a[i]  # i < t 的限制使得每对点只被交换一次（否则交换两次相当于没交换）\n",
    "    # print(\"swap\")\n",
    "    # print(a[:8])\n",
    "\n",
    "    l = 2\n",
    "    while l <= n:   # stage循环 l为该stage子序列的长度\n",
    "        m = l // 2  # 根据FFT特性，后一半的数值可由前一半的中间值计算得出\n",
    "        for p in range(0, n, l):\n",
    "            for i in range(m):\n",
    "                t = omg[n // l * i] * a[p + i + m]\n",
    "                # print('t=',t)\n",
    "                a[p + i + m] = a[p + i] - t\n",
    "                a[p + i] += t\n",
    "        l *= 2\n",
    "        \n",
    "    return a\n",
    "\n",
    "def fft_4k(signal):\n",
    "    xx=[signal[i::4] for i in range(4)]\n",
    "    x=[fft(xx[i]) for i in range(4)]\n",
    "    for ii in range(4):\n",
    "        for jj in range(1024):\n",
    "            x[ii][jj]*=cmath.exp(-2j * cmath.pi * ii*jj / nsamples)\n",
    "    a=[]\n",
    "    for i in range(1024):\n",
    "        res=fft(np.array([x[j][i] for j in range(4)]))\n",
    "        a+=list(res)\n",
    "        if i<4:\n",
    "            print(res)\n",
    "    b=[a[i::4] for i in range(4)]\n",
    "    a=[]\n",
    "    for ii in range(4):\n",
    "        a+=b[ii]\n",
    "    return np.array(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_65935/256779702.py:52: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  a[p + i + m] = a[p + i] - t\n",
      "/tmp/ipykernel_65935/256779702.py:53: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  a[p + i] += t\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.08294446+0.j         6.08310679+0.03706246j 6.08359384+0.07413135j ...\n",
      " 6.08440577-0.11121311j 6.08359384-0.07413135j 6.08310679-0.03706246j]\n"
     ]
    }
   ],
   "source": [
    "signal = generate_signal(nsamples, fs)\n",
    "spectrum, freqs = compute_fft(signal, fs)\n",
    "spectrum_btf = fft(signal)\n",
    "# spectrum_btf=fft_4k(signal)\n",
    "print(spectrum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_65935/921435423.py:3: RuntimeWarning: invalid value encountered in log\n",
      "  plt.plot(freqs / 1e3, 20*np.log(spectrum_btf), label=\"Btf\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fea5053b100>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(freqs / 1e3, 20*np.log(spectrum), label=\"Scipy\")\n",
    "plt.plot(freqs / 1e3, 20*np.log(spectrum_btf), label=\"Btf\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vector2file_cint16(data: np.array, file: str, plio = 128, bits = 16, scale: bool=True):\n",
    "    \"\"\"\n",
    "    data: samples\n",
    "    file: output filename\n",
    "    plio: bit width of the PLIO port\n",
    "    bits: bit precision per sample\n",
    "    \"\"\"\n",
    "    \"\"\"Scale signal to use full precision\"\"\"\n",
    "    maxr = np.max(np.abs(data.real))\n",
    "    maxi = np.max(np.abs(data.imag))\n",
    "    maxv = maxr if maxr > maxi else maxi\n",
    "    if scale:\n",
    "        # vscale = 2**int(np.floor(np.log2((1 << (bits-1)) / maxv)))\n",
    "        vscale = 1<<6\n",
    "    else:\n",
    "        vscale = 1\n",
    "    data = data * (vscale if scale else 1)\n",
    "    \"\"\"Write value to file\"\"\"\n",
    "    with open(file, 'w', newline='') as f:\n",
    "        for i, v in enumerate(data):\n",
    "            r = np.int16(v.real)\n",
    "            c = np.int16(v.imag)\n",
    "            f.write(\"{} {} \".format(r, c))\n",
    "            if (((i+1) % 4) == 0 and plio == 128) or (((i+1) % 2) == 1 and plio == 64):\n",
    "                f.write('\\n')\n",
    "    \n",
    "    return vscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n"
     ]
    }
   ],
   "source": [
    "for i in range(8): \n",
    "    vscale=vector2file_cint16(signal[i::8], 'DataInFFT'+str(i)+'.txt', scale=True)\n",
    "print(vscale)\n",
    "# vector2file_cint16(signal, 'DataInFIR0.txt', scale=True)\n",
    "# vector2file_cint16(spectrum, 'DataOutFFT0.txt', scale=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WAIT! ! ! AIE! ! !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "def read_file(file: str, samples: int):\n",
    "    value = np.zeros(samples, dtype=np.complex128)\n",
    "    count = 0\n",
    "    with open(file,'r') as f:\n",
    "        reader = csv.reader(f, delimiter=\" \")\n",
    "        for line in reader:\n",
    "            if 'T' in line[0]:\n",
    "                continue\n",
    "            for i in range(0, len(line), 2):\n",
    "                if line[i] != '':\n",
    "                    value[count] = np.int16(line[i]) + 1j* np.int16(line[i+1])\n",
    "                    count = count + 1\n",
    "    return value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the output file from the AIE, now using SciPy file for simplicity\n",
    "signal_read = read_file('DataOutFFT0.txt', nsamples)\n",
    "sr=[[] for i in range(8)]\n",
    "for i in range(0,nsamples,4):\n",
    "    sr[(i//4)%8]+=list(signal_read[i:i+4])\n",
    "signal_read=np.concatenate(sr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fea504d1a30>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scaled_in = read_file('DataInFFT0.txt', nsamples)\n",
    "# spectrum, _ = compute_fft(scaled_in,fs)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(freqs / 1e3, 20*np.log(signal_read/vscale), label=\"AIE\")\n",
    "plt.plot(freqs / 1e3, 20*np.log(spectrum), label=\"Scipy\",linestyle='--')\n",
    "#plt.plot(freqs / 1e3, 20*np.log(signal_read), label=\"AIE\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array_equal(spectrum, signal_read)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isclose(spectrum, signal_read, 2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "error = spectrum - signal_read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26538.4739260156"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.abs(error.real))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32773.062521943146"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.abs(error.imag))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy: Max Real 2388.92710446468, Max Imag 2934.883147999578\n",
      "Numpy: Max Real 26964.0, Max Imag 32247.0\n"
     ]
    }
   ],
   "source": [
    "def findmax(fftnp, fftaie):\n",
    "    print(f'Numpy: Max Real {np.max(np.abs(fftnp.real))}, Max Imag {np.max(np.abs(fftnp.imag))}')\n",
    "    print(f'Numpy: Max Real {np.max(np.abs(fftaie.real))}, Max Imag {np.max(np.abs(fftaie.imag))}')\n",
    "\n",
    "findmax(spectrum, signal_read)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "bd76463b23fe99089a42dcbd61c93a219c6242df3d4b53db5f5a167e07e18f3c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
